title: Recursive partitioning analysis of prognostic factors for survival in patients with advanced cancer.
abstract: OBJECTIVE
  To construct a predictive model for survival of patients referred to the Rapid Response Radiotherapy Program using recursive partitioning (RP).
  
  
  METHODS
  We analyzed 16 factors characterizing patients with metastases at first referral to the Rapid Response Radiotherapy Program for palliative radiotherapy in 1999 for their effect on survival. They included age, primary cancer site, site of metastases, weight loss (>or=10% during the past 6 months), Karnofsky performance status (KPS), interval from the first diagnosis of cancer to the first consultation at the Rapid Response Radiotherapy Program, analgesic consumption within the previous 24 h, and the nine symptom scores from the Edmonton Symptom Assessment Scale. We used RP to develop a predictive model of survival for patients referred in 1999, followed by temporal validation using patients referred in 2000, and external validation using patients referred in 2002 to another institution.
  
  
  RESULTS
  The model was able to separate patients into three groups with different durations of survival that were defined by (1) KPS >60; (2) KPS <or=60 with bone metastases only; and (3) KPS <or=60 with other metastases. The model performed moderately well when applied to the validation sets, but a major limitation was that it led to an unequal distribution of patients, with a small proportion of patients in the intermediate group.
  
  
  CONCLUSIONS
  We have demonstrated that RP can be used to predict the survival of patients with advanced cancer. However, this model has no advantages compared with our published prognostic models that use the survival prediction scores and number of risk factors.
authors.affiliation: Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada. Edward.Chow@sunnybrook.ca
authors.forename: Edward
authors.initials: E
authors.lastname: Chow
authors.forename: Mohamed
authors.initials: M
authors.lastname: Abdolell
authors.forename: Tony
authors.initials: T
authors.lastname: Panzarella
authors.forename: Kristin
authors.initials: K
authors.lastname: Harris
authors.forename: Andrea
authors.initials: A
authors.lastname: Bezjak
authors.forename: Padraig
authors.initials: P
authors.lastname: Warde
authors.forename: Ian
authors.initials: I
authors.lastname: Tannock
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001943:Breast Neoplasms / Q000401:mortality / Q000473:pathology / Q000532:radiotherapy; D005260:Female; D006801:Humans; D017567:Karnofsky Performance Status; D008175:Lung Neoplasms / Q000401:mortality / Q000473:pathology / Q000532:radiotherapy; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009369:Neoplasms / Q000401:mortality* / Q000473:pathology / Q000532:radiotherapy; D010166:Palliative Care / Q000379:methods; D011237:Predictive Value of Tests; D011379:Prognosis; D011471:Prostatic Neoplasms / Q000401:mortality / Q000473:pathology / Q000532:radiotherapy; D012044:Regression Analysis; D055815:Young Adult